Zuria Bauer/Daniel Domene López - How to improve your diet and save money with Python
[19 July 2016 / 2016-07-19]
[Bilbao, Euskadi, Spain]
Optimization in Python (also known as mathematical programming) can be
performed by minimization (or maximization) of an objective function
within a model that can include discrete variables subject to a set of
constrains. At this talk, chemical engineering students of the
University of Alicante will introduce the audience to the
possibilities of optimization, presenting Pyomo and showing real world
examples such as how to improve your diet and save money at fast food
Process optimization in industry has become essential in order to
maximize the resources available and reduce energy consumption.
Optimization problems become interesting when dealing with
restrictions (linear or nonlinear) and integer variables (modeling the
discrete decisions). Python ecosystem presents different libraries to
solve optimization problems, some of them are CVXOpt, CVXPy, PulP,
OpenOpt, or Pyomo.
Among them, Pyomo results interesting because:
- It can be used for Mathematical modeling in Python similarly to AMPL
- It communicates with the main solvers used in this field such as
GLPK, Gurobi, CPLEX, CBC and PICO
- It's free and open source Python library (BSD license), being
developed by Sandia National Laboratories, USA.
- It supports Python 3 and it is easy to install.
The talk will be divided in three parts:
1. _Introduction to Mathematical Programming/Optimization (15 min):_
visual introduction to optimization concepts including restrictions
and non linearties (linear Programming, Nonlinear Programming, ILP,
2. _Introduction to the Pyomo sintax and a quick note for the
installation (20 min):_ showing how to improve their diet and save
money when ordering food in fast food restaurants.
3. _Optimization problems in engineering (10 min):_ showing more
advanced optimization examples that include decision variables.